Building a Policy-Governed Multi-Agent Financial Research Workflow with Omnigent
MarkTechPost Sana Hassan
A tutorial demonstrates building a multi-agent financial research system using Omnigent, where a lead agent retrieves live USD-to-EUR exchange rates via API and delegates draft summaries to an auditing sub-agent for validation. The workflow enforces governance policies limiting tool calls to 20 per session and capping API costs at $1.00 USD. The implementation shows how to combine agent delegation, live data access, and cost controls in a single YAML-configured system runnable in Google Colab without additional dependencies.
Why it matters
In this tutorial, we demonstrate how to build and execute a multi-agent workflow with Omnigent in a secure, isolated Python environment. Learn to integrate live exchange-rate data, implement hierarchical agent delegation for financial text auditing, and apply hard governance policies—such as cost budgets and tool call limits—to your research pipeline directly from Google Colab. The post Building a Policy-Governed Multi-Agent Financial Research Workflow with Omnigent appeared first on MarkTechPost.
